Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

The new Streamable HTTP transport replaces the HTTP+SSE transport from protocol version 2024-11-05. This transport enables the MCP server to handle multiple client connections, and can also use SSE for server-to-client streaming. To use it, initialize the MCPTools passing the URL of the MCP server and setting the transport to streamable-http: 
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools

server_url = "http://localhost:8000/mcp"

async def run_mcp_agent():

    # Initialize the MCP tools
    mcp_tools = MCPTools(url=server_url, transport="streamable-http")

    # Connect to the MCP server
    await mcp_tools.connect()

    # Initialize the Agent
    agent = Agent(model=OpenAIChat(id="gpt-4o"), tools=[mcp_tools])

    # Run the agent
    await agent.aprint_response("What is the license for this project?", stream=True)

    # Close the MCP connection
    await mcp_tools.close()
You can also use the server_params argument to define the MCP connection. This way you can specify the headers to send to the MCP server with every request, and the timeout values: 
from agno.tools.mcp import MCPTools, StreamableHTTPClientParams

server_params = StreamableHTTPClientParams(
    url=...,
    headers=...,
    timeout=...,
    sse_read_timeout=...,
    terminate_on_close=...,

)

async def run_mcp_agent():

    # Initialize the MCP tools
    mcp_tools = MCPTools(server_params=server_params)

    # Connect to the MCP server
    await mcp_tools.connect()

    ...

​
Complete example

Let’s set up a simple local server and connect to it using the Streamable HTTP transport:
1

Setup the server

streamable_http_server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("calendar_assistant")


@mcp.tool()
def get_events(day: str) -> str:
    return f"There are no events scheduled for {day}."


@mcp.tool()
def get_birthdays_this_week() -> str:
    return "It is your mom's birthday tomorrow"


if __name__ == "__main__":
    mcp.run(transport="streamable-http")
2

Setup the client

streamable_http_client.py
import asyncio

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools, MultiMCPTools

# This is the URL of the MCP server we want to use.
server_url = "http://localhost:8000/mcp"


async def run_agent(message: str) -> None:

    # Initialize the MCP tools
    mcp_tools = MCPTools(transport="streamable-http", url=server_url)

    # Connect to the MCP server
    await mcp_tools.connect()

    # Initialize the Agent
    agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        tools=[mcp_tools],
        markdown=True,
    )

    # Run the agent
    await agent.aprint_response(message=message, stream=True, markdown=True)

    # Close the MCP connection
    await mcp_tools.close()


# Using MultiMCPTools, we can connect to multiple MCP servers at once, even if they use different transports.
# In this example we connect to both our example server (Streamable HTTP transport), and a different server (stdio transport).
async def run_agent_with_multimcp(message: str) -> None:

    # Initialize the MultiMCPTools instance
    multi_mcp_tools = MultiMCPTools(
        commands=["npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt"],
        urls=[server_url],
        urls_transports=["streamable-http"],
    )

    # Connect to the MCP servers
    await multi_mcp_tools.connect()

    # Initialize the Agent
    agent = Agent(
        model=OpenAIChat(id="gpt-4o"),
        tools=[mcp_tools],
        markdown=True,
    )

    # Run the agent
    await agent.aprint_response(message=message, stream=True, markdown=True)


if __name__ == "__main__":
    asyncio.run(run_agent("Do I have any birthdays this week?"))
    asyncio.run(
        run_agent_with_multimcp(
            "Can you check when is my mom's birthday, and if there are any AirBnb listings in SF for two people for that day?"
        )
    )
3

Run the server

python streamable_http_server.py
4

Run the client

python streamable_http_client.py